The Effects of High Frequency Signal Investigated in a Neuron Model

高频电磁辐射对神经系统有负面影响,并且此类生物实验特别是人体实验会引起潜在的健康危害,而在数学模型中的仿真研究则可以完全避免。通过随机龙格库塔算法求解含有白噪声的HOdgkIn-HuXlEy神经元模型,研究了高频信号对神经元的影响。实验结果表明神经元模型处于其最敏感频率环境中时,随着高频信号的幅频比的提高,模型会产生放电频率下降,随机共振消失等现象,噪声强度较强时还产生了振动共振现象,而且神经元的静息电位也将被高频振荡代替。放电现象消失与生理实验发现的高频电流信号可以阻断外周神经动作电位的传导结果一致,本研究还有助于非电离辐射引起的体内特别是神经系统的损伤的预防和治疗,以及神经系统的模型仿真及其信号处理机制的研究。We have investigated the effects of high frequency (HF) signal on firing activity in a biologically realistic system-the noisy Hodgkin-Huxley (HH) neuron model via numerical simulations.The results show that when the HF amplitude to frequency ratio (AFR) increases,the firing rate is diminished and stochastic resonance disappears,even the HH neuron model is processing a stimulus of its most sensitive frequency.When the noise intensity is strong,the vibration resonance can be observed.Moreover,the fluctuation around the resting potential will be replaced by an oscillation of the same high frequency with the increasing AFR.The inhibition of the firing activity is consistent with the results of experiment in vivo that HF current can stop the transmission of action potential in peripheral nerve.This study is of functional significance to the biomedical research on the damages caused by electro-pollution in vivo and signal processing

Enhancement of Digital Image by Pulse Coupled Neural Networks with Noise

神经系统中广泛存在着噪声,大量研究表明噪声有助于弱信号的检测和传输。脉冲耦合神经网络是建立在生物神经系统上的第三代人工神经网络,被广泛应用于图像处理。为了研究噪声对脉冲耦合神经网络图像处理的影响,通过在网络中引入加性噪声,用于图像增强。直观视觉效果和图像直方图均表明适当的噪声有助于图像增强,噪声过小或过强则减弱图像增强效果;图像的峰值信噪比随噪声强度增强呈现倒钟形,表明存在随机共振现象。本研究表明适当强度的噪声能够提高脉冲耦合神经网络图像处理的效果,并显示出随机共振,有助于开展基于生物神经系统的智能化图像处理方法的研究。Noise has been found in many kinds of neural systems and thought to be helpful in detecting and processing weak signals.The pulse coupled neural network(PCNN) is a kind of artificial neural network based on the biological experiments,and has been widely applied to image processing.To investigate the influence of noise on the image processing by PCNN,we added a noise to the net to realize image enhancement.The images and their histograms showed that noise of suitable intensity was helpful to the image enhancement,while the noise with smaller or stronger intensities produced harmful effects on the results.The curve of peak signal-to-noise ratio(PSNR) versus noise intensity was reversed-bell like,representing the characteristic of stochastic resonance.It was showed that noise could improve the image enhancement by PCNN through stochastic resonance,being beneficial to the neural system for realizing intelligent signal processing

中国海及邻近区域碳库与通量综合分析

中国海总面积约470万平方公里,纵跨热带、亚热带、温带、北温带等多个气候带.其中,南海北依\"世界第三极\"青藏高原、南邻\"全球气候引擎\"西太平洋暖池,东海拥有全球最宽的陆架之一,跨陆架物质运输显著,黄海是冷暖流交汇区域,渤海则是受人类活动高度影响的内湾浅海.中国海内有长江、黄河、珠江等大河输入,外邻全球两大西边界流之一的黑潮.这些鲜明的特色赋予了中国海碳储库和通量研究的典型代表意义.文章从不同海区（渤海、黄海、东海、南海）、不同界面（陆-海、海-气、水柱-沉积物、边缘海-大洋等）,以及不同生态系统（红树林、盐沼湿地、海草床、海藻养殖、珊瑚礁、水柱生态系统等）多层面对海洋碳库与通量进行了较系统地综合分析,初步估算了各个碳库的储量与不同碳库间的通量.就海气通量而言,渤海向大气中释放CO2约0.22Tg Ca-1,黄海吸收CO2约1.15Tg Ca-1,东海吸收CO2约6.92～23.30Tg Ca-1,南海释放CO2约13.86～33.60Tg Ca-1.如果仅考虑海-气界面的CO2交换,中国海总体上是大气CO2的\"源\",净释放量约6.01～9.33Tg Ca-1.这主要是由于河流输入以及邻近大洋输入所致.河流输入渤黄海、东海、南海的溶解无机碳（DIC）分别为5.04、14.60和40.14Tg Ca-1,而邻近大洋输入DIC更是高达144.81Tg Ca-1,远超中国海向大气释放的碳量.渤海、黄海、东海、南海的沉积有机碳通量分别为2.00、3.60、7.40、7.49Tg Ca-1.东海和南海向邻近大洋输送有机碳通量分别为15.25～36.70和43.39Tg Ca-1.就生态系统而言,中国沿海红树林、盐沼湿地、海草床有机碳埋藏通量为0.36Tg Ca-1,海草床溶解有机碳（DOC）输出通量为0.59Tg Ca-1;中国近海海藻养殖移出碳通量0.68Tg Ca-1,沉积和DOC释放通量分别为0.14和0.82Tg Ca-1.总计,中国海有机碳年输出通量为81.72～103.17Tg Ca-1.中国海的有机碳输出以DOC形式为主,东海向邻近大洋输出的DOC通量约15.00～35.00Tg Ca-1,南海输出约31.39Tg Ca-1.综上,尽管从海-气通量看中国海是大气CO2的\"源\",但考虑了河流、大洋输入、沉积输出以及微型生物碳泵（DOC转化输出）作用后,中国海是重要的储碳区.需要指出的是,文章数据是基于中国海各海区碳循环研究报道,鉴于不同研究方法上的差异,所得数据难免有一定的误差范围,亟待将来统一方法标准下的更多深入研究和分析.国家重点研发计划项目(编号:2016YFA0601400);;国家自然科学基金项目(批准号:91751207、91428308、41722603、41606153、41422603);;中央高校基础研究项目(编号:20720170107);;中海油项目(编号:CNOOC-KJ125FZDXM00TJ001-2014、CNOOCKJ125FZDXM00ZJ001-2014)资

The transmission of weak signal in one-way coupled Hodgkin-Huxley neural system

研究了阈下信号在含噪声的HOdgkIn-HuXlEy神经元单向耦合系统中的传输特性.结果表明,各单元中均存在随机共振现象,可见噪声有助于提高信号的检测和传输;另外,耦合实现了信号的传输,且随着耦合强度的增强信号的传输效率增加,在耦合强度达到某一程度时两神经元实现了有时延的一致放电;并且接收元的信噪比最优值处的噪声强度随着耦合强度的提高而减小,最终与驱动元的一致;另外在耦合强度过强时,接收元出现过耦合放电,但是最终会被不断增强的噪声抑制,此现象有助于解释神经元的自放电及神经系统的自调节.研究表明噪声和耦合在神经系统传输弱信号过程中起重要作用,且有助于揭开神经系统的信号传输机理,在模型中解释生物学现象.The study of signal transmission in neural models is helpful to investigate and model the transmission mechanism of biological neural systems,and realize the signal processing of cognition mechanism.We investigate the transmission of weak signals in a noisy one-way coupled Hodgkin-Huxley neural system.The results show that there is stochastic resonance and it helps to detect and transmit the signal.In addition,the coupling enables the signal transmission in the system.The efficiency of the signal transmission is increased with the increasing coupling strength.At some strength the firing of the elements will be in synchronization.What's more,the optimal noise intensity of the receptor declines with the increase of the coupling strength,in the end will be equal to that of the driver.When the coupling strength is too high,the receptor will fire by itself.But the spikes will be suppressed by the increasing noise.It may be used to interpret the self action of neurons and the self adjusting in neural system.In addition,these results show that the noise and coupling are important to the transmission of weak signal in neural system.This study maybe helpful to interpret some phenomena in biological experiments

Transmission of Weak Sinusoidal Signal in the Noisy FN Neuron Model

长期以来弄清神经系统中的信号是如何传输的一直是广大研究人员努力的目标。针对一种被普遍研究的神经元简化模型——FitzHugh-nagumo(FN)模型,采用二阶随机龙格-库塔算法分析了该模型对加性噪声和微弱正弦信号的响应特性。时域和频域的统计参数表明适当强度的噪声有利于信号的传输,存在随机共振现象,即与噪声强度关联的输出信噪比曲线为倒钟形;另外值得关注的是,与正弦信号频率关联的输出信噪比曲线也为倒钟形,分析可见正弦信号的无量纲频率在区间0.2~0.8时模型的输出信噪比最大,表明该神经元模型有频率敏感性,即更易于检测到该范围内的弱信号。上述结果与生物学的发现是一致的,将有助于进一步揭示周期信号在神经元中的传输方法,建立更加准确的神经元数学模型。To make out the signal processing of neuron and neural system,researchers have worked on it for scores of years.In this paper,the responses of FitzHugh-Nagumo(FN) neuron model,which is stimulated by additive Gaussian white noise and weak sinusoidal signal,is investigated via numerical simulations.The time and frequency domain results show that there is stochastic resonance and a dimensionless frequency sensitive range of 0.2-0.8.Thus the model can help to detect the sinusoidal signal.In addition,the frequency sensitivity coincides with the findings in biology and is beneficial to disclosing the secret in the signal transmission of neuron and neural system.国家自然科学基金资助项目(30770561

Research Progress of Stochastic Resonance in Neural Models

随机共振是一种常见于非线性系统中的由适当的噪声引起的系统最优响应现象。神经系统含有噪声,相关生物实验和理论研究均证明噪声有助于神经信号的检测和处理,且当前对神经系统信息处理与存储机制的研究是一大热点。文章回顾了最近发表的关于随机共振的研究成果,从噪声、随机共振的引申概念和网络模型三个方面,总结了目前对神经模型中随机共振的研究进展,并简单讨论了这类研究的发展趋势。In nonlinear systems,noise can improve the responses of the systems with appropriate noise intensity.This phenomenon is called stochastic resonance.Biological neural systems are noisy and stochastic resonance has been found in them experimentally and theoretically.Now many researches focus on the signal transmission and processing in neural models.So this paper introduces the researches of stochastic resonance in noisy neural models.Then the recent research achievement and progress are reviewed in the following three aspects:noise; the development of stochastic resonance; and neural network.At last,the foreground of the study is discussed

隐含语义检索系统词条权重的处理

隐含语义检索技术是一种基于概念的检索方法,本文介绍了隐含语义检索的原理,并考虑到不同词条对文档内容描述重要程度不同,通过提高特征词、关键词的权重改进了隐含语义检索系统。工作中对检索系统中不同重要程度的词条采用了不同的权重算法计算权重,并以化学学科信息门户中的西文期刊简介页作为测试文档进行了检索测试,分析了权重算法改进前后检索测试的数据,结果表明,改进后的隐含语义检索系统的检索效果有了较大的提高